Identification of flow regime in a bubble column reactor with a combination of optical probe data and machine learning technique

被引：22

作者：

Manjrekar O.N. ^{[1
]}

Dudukovic M.P. ^{[1
]}

机构：

[1] Department of Energy, Environmental and Chemical Engineering, Washington University in Saint Louis, Chemical Reaction Engineering Laboratory, 63130, MO

来源：

Chemical Engineering Science: X | 2019年 / 2卷

关键词：

Bubble column; Data analytics; Flow regime identification; Machine learning; Optical probe technique; Support vector machine;

D O I：

10.1016/j.cesx.2019.100023

中图分类号：

学科分类号：

摘要：

In the present work, a data-driven model for identification of flow regime in a bubble column is developed by combining data from optical probe technique and machine learning. Optical probe data from previous work was combined with new data in the present work to expand the database for model development. A novel methodology for determination of two key parameters from the optical probe signal, bubble time and characteristic time of the signal, is presented. The significance of these two parameters is that they contain rich information on operating flow regime in the bubble column. A map of these two parameters for various operating conditions is created, showing points belonging to identical flow regime lie in a cluster. A machine learning methodology based on support vector analysis was developed to identify flow regime using map developed in this work. This approach was able to uniquely classify flow regimes for various experimental conditions on single map, which is the highlight of this work. © 2019

引用

共 73 条

[1]

Azzopardi B.J., Mudde R.F., Lo S., Morvan H., Yan Y., Zhao D., Bubble columns, hydrodynamics of gas-liquid reactors, John Wiley Sons Ltd, pp. 3-59, (2011)

[2]

Besagni G., Di Pasquali A., Gallazzini L., Gottardi E., Colombo L.P.M., Inzoli F., The effect of aspect ratio in counter-current gas-liquid bubble columns: experimental results and gas holdup correlations, Int. J. Multiph. Flow, 94, pp. 53-78, (2017)

[3]

Besagni G., Inzoli F., The effect of electrolyte concentration on counter-current gas–liquid bubble column fluid dynamics: gas holdup, flow regime transition and bubble size distributions, Chem. Eng. Res. Des., 118, pp. 170-193, (2017)

[4]

Boukouvala F., Muzzio F.J., Ierapetritou M.G., Dynamic data-driven modeling of pharmaceutical processes, Ind. Eng. Chem. Res., 50, pp. 6743-6754, (2011)

[5]

Chen R.C., Reese J., Fan L.-S., Flow structure in a three-dimensional bubble column and three-phase fluidized bed, AIChE J., 40, pp. 1093-1104, (1994)

[6]

Chen J., Yang N., Ge W., Li J., Computational fluid dynamics simulation of regime transition in bubble columns incorporating the dual-bubble-size model, Ind. Eng. Chem. Res., 48, pp. 8172-8179, (2009)

[7]

Chiang L.H., Leardi R., Pell R.J., Seasholtz M.B., Industrial experiences with multivariate statistical analysis of batch process data, Chemometr. Intell. Lab. Syst., 81, pp. 109-119, (2006)

[8]

Chiang L., Lu B., Castillo I., Big data analytics in chemical engineering, Ann. Rev. Chem. Biomole. Eng., 8, pp. 63-85, (2017)

[9]

Clift R., Grace J.R., Weber M.E., Bubbles Drops and Particles, (1978)

[10]

Colegrove L., Seasholtz M., Khare C., Big data: getting started on the journey, CEP Magaz., pp. 41-45, (2016)

← 1 2 3 4 5 6 7 8 →